Pedestrian detection based on improved color self-similarity feature

doi:10.11772/j.issn.1001-9081.2014.07.2033

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (7): 2033-2035.DOI: 10.11772/j.issn.1001-9081.2014.07.2033

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Pedestrian detection based on improved color self-similarity feature

GU Huijian,CHEN Junzhou

School of Information Science and Technology, Southwest Jiaotong University, Chengdu Sichuan 610031, China

Received:2014-01-15 Revised:2014-03-03 Online:2014-08-01 Published:2014-07-01
Contact: GU Huijian
Supported by:
National Natural Science Foundation of China

基于改进颜色自相似特征的行人检测方法

顾会建,陈俊周

西南交通大学信息科学与技术学院，成都 610031

通讯作者: 顾会建
作者简介:顾会建(1989-)，男，河南濮阳人，硕士研究生，主要研究方向：计算机视觉、目标检测；陈俊周(1979-)，男，四川成都人，副教授，博士， CCF会员，主要研究方向：计算机视觉、模式识别、机器学习。
基金资助:
国家自然基金资助项目

Abstract

Abstract:

In recent years, multiscale pedestrian detection received extensive attentions in the field of computer vision. In traditional methods, the input image must be resized with different scales to compute the features, which significantly reduces the detection speed. Color Self-Similarity Feature (CSSF) was presented to overcome this problem. An improved CSSF with lower dimension was proposed for the CSSF whose dimension is too high and time-consuming in the training process of the classifiers. Combined with pedestrian structural similarity, a fixed-size window was defined at first, and then the improved CSSF was extracted by sliding the fixed-size window in different color space. Finally, the pedestrian detection classifier was constructed by combining with AdaBoost algorithm. Test shows that compared with the traditional CSSF whose dimension is ten millions, new feature dimension is only a few thousand, and it can be extracted and trained faster, but detection effect decreases slightly; compared with the Histogram of Oriented Gradient (HOG), feature extraction speed improves 5 times, detection effect is essentially the same. The new method has a good application value in real-time pedestrian detection and monitoring systems.

摘要：

近年来多尺度行人检测在计算机视觉领域受到广泛关注。传统方法需对图像缩放，在不同尺度计算特征，大大降低了行人检测的速度。颜色自相似特征(CSSF)被提出以克服此不足。针对颜色自相似度特征具有维度高和分类器训练时间长等问题,提出一种改进的颜色自相似度特征。改进的颜色自相似度特征结合行人结构相似度，首先定义了固定尺寸的窗口，然后在不同的颜色空间滑动固定大小的窗口进行特征提取，最后结合自适应增强(AdaBoost)算法构建行人检测分类器。实验结果显示：相对于传统颜色自相似度特征的千万级维度，新的特征只有几千维，特征提取速度和分类器训练速度显著提高，检测效果略有下降；与梯度方向直方图特征(HOG)相比，特征提取速度提高5倍，检测效果基本不变，新的方法在实时行人检测和监控系统中有很好的应用价值。

CLC Number:

TP391.4

GU Huijian CHEN Junzhou. Pedestrian detection based on improved color self-similarity feature[J]. Journal of Computer Applications, 2014, 34(7): 2033-2035.

顾会建陈俊周. 基于改进颜色自相似特征的行人检测方法[J]. 计算机应用, 2014, 34(7): 2033-2035.

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Pedestrian detection based on improved color self-similarity feature

基于改进颜色自相似特征的行人检测方法

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